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Abstract:

The invention discloses an epileptic event alert system, capable of
detecting and analyzing whether motions sensed by at least one motion
sensor of the system, are related to an epileptic seizure event. The
system may be utilized for detection of additional motion-related
pathologies. A detection method is similarly disclosed, as is
computer-readable media adapted to perform the detection method of the
invention.

Claims:

1) An epileptic event alert system comprising:A detection and analysis
unit comprising:a) one or more motion sensors adapted to produce an
electrical signal corresponding with mechanical movement of the detection
and analysis unit;b) a microcontroller comprisingiii. non-volatile memory
adapted to store at least one set of motion signal parameters associated
with epileptic motion and at least another set of parameters associated
with non-epileptic motion;iv. computer readable software and dedicated
hardware adapted to compare at least one signal parameter of the signal
produced by said one or more sensors against at least one of said stored
sets of motion signal parameters;c) a communication unit adapted to
transmit an alert signal to a remote location;d) a control circuit
adapted to interact with said communication unit, and adapted to activate
said computer readable software (ii) upon the signal meeting a threshold
level.

2) The system according to claim 1, wherein each motion sensor produces a
separate signal, and wherein the separate signal produced by each given
sensor includes information relating to motion of said sensor in a
direction corresponding with an orientation of the given sensor.

3) The system according to claim 2, wherein any one or more of the
separate signals reaching the threshold level causes said control circuit
to activate said computer readable software (ii), and wherein said
control circuit induces a power-saving mode in said system when the
signal has not reached the threshold level for a predefined duration
time.

4) The system according to claim 2, wherein the signal produced by said
one or more motion sensors is an analog signal and system further
comprises an analog to digital converter.

5) The system according to claim 1, wherein said software (ii) is adapted
to output a seizure probability value based on a comparison of parameters
of the signal produced by said one or more sensors and the stored set of
motion signal parameters associated with epileptic motion.

6) The system according to claim 5, wherein the seizure probability value
is additionally based on a comparison of parameters of the signal
produced by said one or more sensors and the stored set of motion signal
parameters associated with non-epileptic motion, and wherein the seizure
probability value is positively related to a correlation between the
sensor signal parameter with one or more parameters in the parameter set
associated with epileptic movement and inversely related to a correlation
between the sensor signal parameter with one or more parameters in the
parameter set associate with non-epileptic movement.

7) The system according to claim 5, further comprising an alert decision
unit adapted to produce a local alert signal based on the seizure
probability value and based on a predefined duration of signal.

8) The system according to claim 7, wherein the local alert signal
produced by said decision unit is adapted to trigger said communication
unit to transmit a remote alert signal.

9) The system according to claim 7, further comprising a switch operable
by the user, for deactivating a local alert signal prior to a remote
alert signal being triggered.

10) The system according to claim 1, wherein said communication unit
utilizes a communication method selected from the group consisting of:
Bluetooth, WiFi, ZigBee, and GPRS; and wherein said alert signal is
transmitted to a remote location via one of the following: a modem, a
telephone network, and a cellular telephone network.

11) The system according to claim 1, further comprising an output signal
for instructing an epileptic treatment unit to administer an epileptic
treatment in response to an alert signal.

12) The system according to claim 11, wherein said epileptic treatment
unit applies a treatment substantially automatically in response to
either a local or remote alert signal.

13) The system according to claim 11, wherein said epileptic treatment
unit is adapted to be triggered by a treatment signal initiated remotely
and received through said communication unit.

14) The system according to claim 1, further comprising a visual display
activated by said control circuit; and wherein the microcontroller is
adapted to initiate a self-test.

15) The system according to claim 1, further comprising a microphone for
detecting sounds originating in the vicinity of the user, and said
communication unit is adapted to transmit said sounds detected by said
microphone.

16) The system according to claim 1, wherein said control circuit is
adapted to trigger an alert when no motion signals are detected during a
predefined period of time.

17) The system according to claim 1, wherein said signal parameters
produced by said motion sensor and said stored epileptic motion signal
parameters, are selected from at least one of the following group: the
frequency of the motion, frequency variation over time, the amplitude of
the signal, amplitude variations over time, the relative direction of the
motion, the direction variation over time, and the duration of the
motion.

18) The system according to claim 1, wherein said system is adapted to be
worn upon the limb of a user.

19) The system according to claim 18, wherein said system has the general
appearance of a wristwatch.

20) A method of detecting an epileptic seizure comprising:a. fastening at
least one motion sensor to the limb of a user, said sensor adapted to
produce an electrical signal corresponding with mechanical movement of
the sensor;b. measuring said electrical signal produced by said at least
one motion sensor and performing computerized processing of said signal
to obtain signal motion parameters;c. comparing said parameters of said
measured electrical signal, against at least one stored set of motion
signal parameters and/or against at least one set of non-epileptic
epileptic motion signal parameters;wherein said comparison is performed
using computerized processing means;d. outputting a seizure probability
value based on said comparison;e. transmitting an alert signal to a
remote location, using a communication unit, if said seizure probability
value is within a predetermined range of values.

21) The method according to claim 20, wherein said signal parameters
measured by said motion sensor and said stored epileptic motion signal
parameters, are comprised of at least one of the following: the frequency
of the motion, frequency variation over time, the amplitude of the
signal, amplitude variations over time, the relative direction of the
motion, the direction variation over time, and the duration of the
motion.

22) The method according to claim 20, further comprising a step of
autocorrelation, performed during said step (b), wherein said
autocorrelation is performed upon the signal measured by each sensor, or
performed upon signals measured by different sensors of said system.

23) The method according to claim 20, further comprising a step of removal
of DC bias, performed during said step (b).

25) The computer-readable media according to claim 24, further adapted to
initiate transmission of an alert signal to a remote location, using a
communication unit, if said seizure probability value is within a
predetermined range of values.

26) The system according to claim 1, further comprising a recorder,
adapted to store and allow retrieval of at least one of the following:
motion signal parameters of a user, processed motion signal parameters,
and alert signal transmission data.

27) An alert system for detection and identification of pathological
motion events, comprising:A detection and analysis unit comprising:a) one
or more motion sensors adapted to produce an electrical signal
corresponding with mechanical movement of the detection and analysis
unit;b) a microcontroller comprisingv. non-volatile memory adapted to
store at least one set of motion signal parameters associated with
pathological motion and at least another set of parameters associated
with normal non-pathological motion;vi. computer readable software and
dedicated hardware adapted to compare at least one signal parameter of
the signal produced by said one or more sensors against at least one of
said stored sets of motion signal parameters;c) a communication unit
adapted to transmit an alert signal to a remote location; andd) a control
circuit adapted to interact with said communication unit, and adapted to
activate said computer readable software (ii) upon the signal meeting a
threshold level.

Description:

FIELD OF THE INVENTION

[0001]The present invention relates generally to the field of monitoring
devices. More specifically, the present invention relates to a system and
method for detecting epileptic events in a user.

BACKGROUND

[0002]Epilepsy, a neurological disorder characterized by the occurrence of
seizures (specifically episodic impairment or loss of consciousness,
abnormal motor phenomena, psychic or sensory disturbances, or the
perturbation of the autonomic nervous system), is debilitating to a great
number of people. The prevalence of epilepsy is 0.7% of the population
with as many as two million Americans that suffer from various forms of
epilepsy and around 50 million worldwide. Research has found that its
prevalence may be even greater worldwide, particularly in less
economically developed nations, suggesting that the worldwide figure for
epilepsy sufferers may be in excess of one hundred million.

[0003]A typical epilepsy patient experiences episodic attacks or seizures,
which are generally defined as periods of abnormal neurological activity.
The characteristics of an epileptic seizure onset are different from
patient to patient, but are frequently consistent from seizure to seizure
within a single patient. Because epilepsy is characterized by seizures,
its sufferers are frequently limited in the kinds of activities they may
participate in. Epilepsy can prevent people from driving, working, or
otherwise participating in much of what society has to offer. Some
epilepsy sufferers have serious seizures so frequently that they are
effectively incapacitated. Furthermore, epilepsy is often progressive and
can be associated with degenerative disorders and conditions. Over time,
epileptic seizures often become more frequent and more serious, and may
lead to deterioration of other brain functions (including cognitive
function) as well as physical impairments.

[0004]Timely detection of seizures allows a caregiver to monitor their
severity and duration and to determine whether immediate treatment is
necessary. Attempts have been made to create alarm systems based on
motion systems, which alert a caregiver or call for emergency services in
response to a repetitive rhythmic movement, which could indicate a
seizure. One example is described in U.S. Pat. No. 6,361,508. However,
these systems suffer from an abundance of false alarms, since rhythmic
movement is also associated with many types of everyday activity, such as
walking, hand gesturing, and even typing. Most known systems are placed
under the mattress of a patient, and are unsuitable for wear during an
active day. An example is described in U.S. Pat. No. 4,320,766.

[0005]Nijsen et al. compared the efficiency of accelerometers to detect
seizures to that found using EEG and video readings (Nijsen et al.,
Epilepsy & Behavior 7, (2005), 74-84), and stated that accelerometers do
not require patients to be stationary as does EEG equipment, which is
most readily available in hospitals. Nijsen performed visual analysis of
the plotted signals (as presented on a chart recorder) but did not
perform any numerical or statistical analysis on the accelerometer
readings which would allow an accelerometer to be used as a stand-alone
detection method. Use of an accelerometer alone, without statistical
analysis, would result in a high degree of false positives due to
rhythmic movement present in many everyday activities. As stated in
Nijsen, "Visual analysis of ACM [accelerometer] readings is very labor
intensive . . . it is more difficult to find suitable parameters that
make computerized detection possible". Nijsen therefore recognized the
need to develop a computerized system which would serve as an alert
system allowing normal ambulation.

[0006]Other systems, such as WO 03/001996, necessitate implanted
electrodes, and rely on EEG readings obtained from the brain. Another
type of system is described in WO 02/082999A1, which describes
computerized analysis of video images taken of the patient in order to
determine whether movement shown in a series of images is similar to that
of an epileptic seizure.

[0007]The need exists for an improved method, and system for detecting an
epileptic event in an epilepsy sufferer. The system should not interfere
with everyday activities, and should allow freedom of movement. Such an
improved system should have a low rate of false positive alerts, yet
should successfully detect epileptic seizures when they occur.

SUMMARY OF THE INVENTION

[0008]An epileptic event alert system comprising:

[0009]A detection and analysis unit comprising: [0010]a) one or more
motion sensors adapted to produce an electrical signal corresponding with
mechanical movement of the detection and analysis unit; [0011]b) a
microcontroller comprising [0012]i. non-volatile memory adapted to store
at least one set of motion signal parameters associated with epileptic
motion and at least another set of parameters associated with
non-epileptic motion; [0013]ii. computer readable software and dedicated
hardware adapted to compare at least one signal parameter of the signal
produced by said one or more sensors against at least one of said stored
sets of motion signal parameters; [0014]c) a communication unit adapted
to transmit an alert signal to a remote location; [0015]d) a control
circuit adapted to interact with said communication unit, and adapted to
activate said computer readable software (ii) upon the signal meeting a
threshold level.

[0016]According to one embodiment, each motion sensor produces a separate
signal, and the separate signal produced by each given sensor includes
information relating to motion of the detection and analysis unit in a
direction corresponding with an orientation of the given sensor. In such
case, any one or more of the separate signals reaching the threshold
level causes the control circuit to activate said the software for signal
processing. The control circuit induces a power-saving mode in said
system when the signal has not reached the threshold level for a
predefined duration time.

[0017]Moreover, in certain embodiments, the signal produced by said one or
more sensors is an analog signal and said signal processing unit further
comprises an analog to digital converter.

[0018]In the preferred embodiment, the signal processing software is
adapted to output a seizure probability value based on a comparison of
parameters of the signal produced by said one or more sensors and the
stored set of motion signal parameters associated with epileptic motion.
The seizure probability value is additionally based on a comparison of
parameters of the signal produced by said one or more sensors and the
stored set of motion signal parameters associated with non-epileptic
motion. The seizure probability value is positively related to a
correlation between the sensor signal parameter with one or more
parameters in the parameter set associated with epileptic movement. The
seizure probability value is inversely related to a correlation between
the sensor signal parameter with one or more parameters in the parameter
set associate with non-epileptic movement.

[0019]In the preferred embodiment, the system further comprises an alert
decision unit adapted to produce a local alert signal based on the
seizure probability value, and based on a predefined duration of signal.
In such case, the local alert signal produced by said decision unit is
adapted to trigger said communication unit to transmit a remote alert
signal. Preferably, the system further comprises a switch operable by the
user, for deactivating a local alert signal prior to a remote alert
signal being triggered.

[0020]Additionally, in certain embodiments, the communication unit
includes a communication circuit selected from the group consisting of a
Bluetooth circuit, WiFi circuit, a ZigBee, and a GPRS circuit.

[0021]Moreover, the system may further comprise an output signal for
instructing an epileptic treatment unit to administer an epileptic
treatment in response to an alert signal. The epileptic treatment unit
may apply a treatment substantially automatically in response to either a
local or remote alert signal, or may be adapted to be triggered by a
treatment signal initiated remotely and received through said
communication unit.

[0022]Preferably, the controller is adapted to initiate a self-test.

[0023]Additionally, the system may further comprise a microphone for
detecting sounds originating by the user and from the vicinity of a user,
and said communication unit is adapted to transmit said sounds detected
by said microphone.

[0024]Preferably, said control circuit is adapted to trigger a "no motion
alert" when no motion signals are detected during a predefined period of
time.

[0025]Preferably, the signal parameters produced by said motion sensor and
said stored epileptic motion signal parameters, are selected from at
least one of the following group:

the frequency of the motion, frequency variation over time, the amplitude
of the signal, amplitude variations over time, the relative direction of
the motion, the direction variation over time, and the duration of the
motion.

[0026]In certain embodiments, the system is adapted to be worn upon the
limb of a user. The system may have the general appearance of a
wristwatch.

[0027]The present invention additionally provides a method of detecting an
epileptic seizure comprising: [0028]a) fastening at least one motion
sensors to the limb of a user, said sensor adapted to produce an
electrical signal corresponding with mechanical movement of the sensor;
[0029]b) measuring said electrical signal produced by said at least one
motion sensor and performing computerized processing of said signal to
obtain signal motion parameters; [0030]c) comparing said signal
parameters of said measured electrical signal, against at least one
stored set of epileptic motion signal parameters and/or against at least
one set of non-epileptic epileptic motion signal parameters; wherein said
comparison is performed using computerized processing means; [0031]d)
outputting a seizure probability value based on said comparison; [0032]e)
transmitting an alert signal to a remote location, using a communication
unit, if said seizure probability value is within a predetermined range
of values.

[0033]In the method, preferably, the signal parameters measured by said
motion sensor and said stored epileptic motion signal parameters, are
comprised of at least one of the following:

the frequency of the motion, frequency variation over time, the amplitude
of the signal, amplitude variations over time, the relative direction of
the motion and direction variations, and the duration of the motion.

[0034]In certain embodiments, the method further comprises a step of
autocorrelation, performed during said step (b). Said autocorrelation may
be performed upon a single measured by each sensor, and/or may be
performed upon signals measured by different sensors within the system.

[0035]In other embodiments, the method further comprises a step of removal
of DC bias, performed during said step (b).

[0037]In certain embodiments, the computer-readable media is further
adapted to control and initiate transmission of an alert signal to a
remote location, using a communication unit, if said seizure probability
value is within a predetermined range of values.

BRIEF DESCRIPTION OF THE DRAWINGS

[0038]The subject matter regarded as the invention is particularly pointed
out and distinctly claimed in the concluding portion of the
specification. The invention, however, both as to organization and method
of operation, together with objects, features, and advantages thereof,
may best be understood by reference to the following detailed description
when read with the accompanying drawings in which:

[0039]FIG. 1 shows an overview of the system according to the present
invention.

[0040]FIG. 2A is a block diagram of the system, depicting sensors, a
microphone, a control circuit, a signal processing unit running the
software of the invention, non-volatile memory and a communication unit.

[0041]FIG. 2B illustrates the system, in which a detection and analysis
unit communicating with several alert units, each having a communication
unit (specifically a radio module) for communicating with the detection
and analysis unit and for transmitting the alert signal to a remote
location via telephone. In this embodiment, the detection and analysis
unit transmits an alert to the alert units, via radio such as a local
area network .

[0042]FIG. 3 illustrates a Table of parameters associated with typical
epileptic seizure motions, as compared with the parameters of normal
daily movement.

[0043]FIG. 4 illustrates additional signal parameters measured by a motion
sensor, for specific types of epileptic seizures, as compared to the
signal parameters measured for different types of normal daily
activities.

[0044]FIG. 5 shows a block diagram illustrating functional blocks of
signal processing according to some embodiments of the present invention;

[0045]FIG. 6 shows a flow diagram including the step of a method of signal
processing which may be performed according to some embodiments of the
present invention;

[0046]FIGS. 7A and 7B depict examples of epileptic accelerometer
recordings illustrating epileptic motion signal parameters which may be
processed according to some embodiments of the present invention;

[0047]FIG. 8A shows a "Beating on the table" signal resulting from normal
activity.

[0058]FIG. 18 illustrates a non-epileptic recording of typing on a
keyboard.

[0059]FIG. 19 illustrates parameters and analysis of FIGS. 16, 18.

[0060]It will be appreciated that for simplicity and clarity of
illustration, elements shown in the figures have not necessarily been
drawn to scale. For example, the dimensions of some of the elements may
be exaggerated relative to other elements for clarity. Further, where
considered appropriate, reference numerals may be repeated among the
figures to indicate corresponding or analogous elements.

DETAILED DESCRIPTION OF THE INVENTION

[0061]In the following detailed description, numerous specific details are
set forth in order to provide a thorough understanding of the invention.
However, it will be understood by those skilled in the art that the
present invention may be practiced without these specific details. In
other instances, well-known methods, procedures, components and circuits
have not been described in detail so as not to obscure the present
invention.

[0062]Unless specifically stated otherwise, it is appreciated that
throughout the specification, the terms "processing", "computing",
"calculating", "determining", or the like, refer to the action and/or
processes of a computer or computing system, or similar electronic
computing device. Such action manipulates and/or transforms data
represented as physical, such as electronic, quantities within the
computing system's registers and/or memories into other data similarly
represented as physical quantities within the computing system's
memories, registers or other such information storage, transmission or
display devices.

[0063]The present invention provides an epileptic event alert system,
which is a capable of measuring the extent and relative direction of
motion an individual is currently undergoing. The system is capable of
analyzing whether the motion is similar to that occurring in a seizure
and dissimilar to that seen in a plurality of everyday activities which
an individual may undertake. The system utilizes computerized processing
to evaluate the motion characteristics, and to output a seizure
probability value. If the value is within a predetermined range, the
system causes an alert signal to be transmitted to a remote location via
a communication unit. The alert signal may be transmitted to the
individual's family, to medical personnel or to emergency services.

[0064]Optionally, the system may transmit a local alert signal which the
individual can switch off in case of a false alarm, before the alert is
transmitted to a remote location. A predefined time is allowed to pass
before the remote alert is sent, to allow the user sufficient time to
deactivate a false alarm.

[0065]Referring to FIG. 1, an overview of the system is shown. The central
component of the system is termed the "detection and analysis unit" 100,
and may have the form of a wristwatch, which the patient 102 wears upon a
limb (arm or leg). Preferably, the individual is aware from previous
seizures which limb tends to undergo the most movement during a seizure
(the "predominant seizure limb"), and attaches the detection and analysis
unit 100 to this limb. The detection and analysis unit 100 preferably
contains a 3D accelerometer, generally termed a "motion sensor", capable
of producing an appropriate electrical signal corresponding with
mechanical movement of the sensor in any given direction. Alternatively,
a 2D or 1D accelerometer may be used. The extent of the motion is
measured and analyzed using several parameters, discussed hereinbelow.
The processing unit present within the detection and analysis unit 100
utilizes the software of the invention to compare these parameters to at
least one set of parameters associated with an epileptic seizure, and/or
to at least one set of parameters associated with normal activity. A
seizure probability value is reached and outputted. Optionally, the value
may be outputted to a display as well. If the value is within a
predetermined range, an alert signal is sent in a wireless manner via a
communication unit to a local alert unit 104 present in the patient's
house. The alert unit 104 communicates with the communication unit, which
in turn, utilizes a telephone to reach emergency services 106, or the
patient's family 108.

[0066]Referring to FIG. 2A, the components of the detection and analysis
unit are shown in a block diagram.

[0067]A movement activity detector 22 is shown, connected to a 3D
accelerometer, also known more generally as a motion sensor 21. The
movement activity detector 22 will detect movements and will activate the
central components of the detection analysis unit only when the motion
signal measured by the accelerometer passes a minimal threshold. This
results in power saving, since minimal components of the system are
active during periods of low motion.

[0068]Preferably, the accelerometer is a MEMS (micro-electro-mechanical
device) such as ADXL330 produced by Analog Devices, USA.

[0069]The motion sensor 21 outputs a separate electrical signal for
movement in each of the X, Y, Z axes. The X Y Z analog outputs are fed to
three `Analog to Digital` converters. The analog to digital conversion
process may take place within a general processor or within the motion
sensor.

[0070]A microcontroller 32 is in communication with all other components
of the system, and is configured to oversee the control functions 23 such
as the maintenance and operation software, drivers, interfaces, etc. When
the signal is sent from the motion sensors 21, and is sensed by the
movement activity system 22 to be over a certain threshold, the signal
processing unit 24 is triggered to activate the software of the invention
and begin signal analysis. The parameters of the movement are compared to
sets of parameters stored in non-volatile memory. The stored parameters
are associated with either known epileptic seizures, or are associated
with non-epileptic movements. Sets of stored parameters are illustrated
in the Figure as "movement generic models" 25. Signal processing will be
performed by the software using associated hardware, so that after the
comparison is performed, a seizure probability is outputted and a
decision is reached by the microcontroller 32 whether to activate a local
alert 16 or an epilepsy attack alert 11, which is transmitted via the
radio module 31 to an alert unit (shown in FIG. 1).

[0071]The alert unit includes communication means to transmit the alert
via telephone or cellular telephone networks, to a remote location at
which medical personnel or family members are present.

[0072]The radio module 31 acts as a transmitter/receiver. In a preferred
embodiment, an RF Activity LED is present on the detection and analysis
unit and will indicate when the communication link with the alert unit is
operative. A breakage in this link will cause a specific alarm to be
sounded.

[0073]Optionally, a microphone 30 is present in the detection and analysis
unit, and is activated when an alert is activated, to transmit sounds
produced by the user to a remote location. The sounds may be indicative
of a seizure.

[0074]Optionally, a self-test 17 may be performed in which an end-to-end
assessment of the system ascertains that the system and the radio link
between the detection and analysis unit, and the alert unit, is active.
The self-test additionally checks most of the circuits in these units.
The test is initiated by the user. When initiated, a `Self-test Radio
Message` [14] is sent to the alert units (not shown). The alert units
will perform a local S.T. procedure and will respond back to the
detection and analysis unit. If this response reaches the detection and
analysis unit, LED lights or a buzz will be turned on to indicate that
the overall system is OK.

[0075]Optionally, a USB connection 18 is provided to allow data to be
transferred from the detection and analysis unit to a computer or to
magnetic media. Additionally, software can be loaded and system settings
can be inputted via the USB connection.

[0076]An on/off switch 19 is included.

[0077]In order to ascertain that the user is indeed wearing the detection
unit at all times, the system outputs a "no movement" signal 12 when no
movement is measured by the motion sensors 21 over a predetermined amount
of time. A "no movement alert" 12 is signaled audibly in such case at the
alert unit.

[0078]Rechargeable batteries 20 are used, which may be attached to an
adaptor for recharging.

[0079]Power management 29 circuitry sends the signal processing 24
software and other elements into power-saving mode when the signal has
not reached the threshold level for a predefined duration time. This
results in saving and is advantageous over prior-art systems, since it
allows long-life of the battery before the need to recharge or change the
battery. A relatively small battery may now be used, and the detection
and analysis unit can be wearable and not uncomfortable or overly large.

[0080]A recorder 28 may be included in the detection and analysis unit,
allowing the processed and analyzed data to be saved and retrieved in
future by medical personnel.

[0081]The system may operate to cover an area equal to that found in a
typical house. The alert unit includes communication means, which sends
the signal to a more remote location, such as to a hospital, off-site
relatives, etc.

[0082]The communication unit may include a communication circuit selected
from: a Bluetooth circuit, WiFi circuit, a ZigBee, and a GPRS circuit or
standard radio devices.

[0083]In certain embodiments, the system may activate a cellular
telephone, or may contain circuitry having cellular-like and GPS
qualities capable of contacting a cellular network for transmitting the
alert and patient location to a remote location. This would allow the
detection and analysis unit to be worn and be operative in various
locations other than the user's own home.

[0084]Optionally, in addition to outputting an alert signal, the system
may output a signal to an epileptic treatment unit in the patient's
vicinity, instructing the treatment unit to administer an epileptic
treatment. Such a treatment may constitute for example, administration of
an injected drug, or administration of an electric shock.

[0085]Certain parameters of the signal produced by the motion sensor,
undergo analysis and computerized comparison to stored parameters of
known epileptic motion, and comparison to non-epileptic motion. This
allows determination whether the motion is a seizure or not. In the
preferred embodiment, the parameters comprise the following:

the frequency of the motion, frequency variation over time, the amplitude
of the signal, amplitude variations over time, the relative direction of
the motion and direction variations, and the duration of the motion.

[0086]Referring to FIG. 2B, the detection and analysis unit 100 is shown,
along with several alert units 102a, 102b, 102c, which contain
communication units which transmit the alert signal to a remote location.
In this embodiment, the detection and analysis unit 100 transmits a local
alert via a local area network to the alert units 102 which are scattered
within the user's house for optimal area coverage. At least one of the
alert units 102(a,b,c) may be connected via a land-line or via cellular
phone, to the telephone network, and communicates with medical personnel
or family members upon signaling of an alert. The alert units 102(a,b,c)
contain radio modules 104(a,b,c), and microcontrollers 106(a,b,c) having
relevant software and hardware capabilities allowing transmission of an
alert message. Included is an interface 108 with the telephone line.

[0087]Optionally, the detection and analysis unit outputs a signal to an
epileptic treatment unit 110, which may administer an injected drug or an
electrical shock to the patient, either in response to the alert, or in
response to a signal received from remote medical personnel (via the
alert unit) after they have noted transmission of the remote alert.

[0088]The system can optionally evaluate how critical the situation of the
patient is (e.g. long attacks--"Status Epilepticus") and transmit a
message in order to enable auto-medication.

[0089]Referring to FIG. 3, a Table is shown in which the parameters of
typical epileptic seizure motions are compared with the parameters of
normal daily movement. Note the duration of the movement is typically
long in epileptic seizures (over 20 seconds, lasting up to minutes or
hours). The repetitive pattern of movement is typically such that a 2 to
15 second window of time is typically used for analysis and
identification. In comparison, movement may be brief in normal activity.
Note that the direction of the movement is random in seizures, including
movement repetitive in many directions. In comparison, repetitive
movement stemming from normal activity will usually involve a specific
direction, as may be envisioned during walking.

[0090]Referring to FIG. 4, additional signal parameters measured by a
motion sensor are shown for specific types of epileptic seizures, as
compared to the signal parameters measured for different types of normal
daily activities. Normal activities that need to be ruled out during
seizure detection, include walking, exercising, talking using hand
gestures, and stretching. Parameters of the signals for these activities
differ from those of epileptic seizures (e.g. clonic, tonic seizures,) as
is evident from FIG. 4, and computerized analysis using the invention can
successfully match the parameters to epileptic or non-epileptic motion.

[0091]Referring to FIG. 5, a flow diagram is shown indicating the central
steps of the motion signal processing.

[0092]Referring to FIG. 6, a more detailed diagram is shown, allowing for
instance "fuzzy logic" to be used during processing, to match the motion
parameters of a user, as recorded at a given time, to one of the
pre-stored sets of known parameters ("generic models"). The pre-stored
sets of motion parameters represent typical epileptic motion parameters,
and/or non-epileptic (healthy) motion parameters. The seizure probability
value is calculated first using short windows of time, and if the value
is insufficient to allow an alert/non-alert decision to be reached, the
analysis will continue for longer periods of time until a decision can be
reached.

[0094]In this and in other Figures, for clarity, only one channel is
plotted, illustrating movement in a single axis of the X, Y, Z
directional axis.

[0095]FIG. 7A demonstrates part of a clonic seizure. A clonic jolt has
impulse characteristics accompanied by relaxation vibration at the tonic
frequency range. This type of seizure is associated with medium frequency
(slower frequency than tonic seizures, quicker frequency than regular
movements). The jolt has relatively high amplitude. Its basic frequency
range can be in the range of 0.5-3 Hz. For example, in the grand
tonic-clonic example shown in FIG. 7A, the frequency is approximately 3
Hz, and the relative amplitude voltage is in the range of 40-80.

[0096]FIG. 7B shows a tonic seizure. It is characterized by fluctuations
at high frequencies (such as 5-15 Hz) and medium amplitude (higher than
regular movements). For example, in FIG. 7b, the frequency is
approximately 6 Hz, and the relative amplitude voltage is approximately
5-15.

[0097]The graphs shown in FIGS. 8 and 9 are examples of non-epileptic
acceleration recordings. For clarity, movement in only one channel
(direction) is plotted.

[0098]FIG. 8A shows a "Beating on the table" signal. It is a "clonic like"
movement: at medium frequency with very high amplitude.

[0099]FIG. 8B illustrates normal movement occurring during a
"Night-Stretch" (stretching of the arms, as occurs during normal sleep).
It is a "tonic like" motion: at high frequency and medium to low
amplitude.

[0100]FIG. 8C shows zooming in on this signal. The resemblance to
epileptic acceleration signal can be seen. Each graph originates in an
accelerometer reading acceleration in a different axis. As in epileptic
motion, this movement changes its direction with time: at the beginning,
it is mainly in the X, and a bit in the Y direction, while later, the Y
axis becomes the dominant direction. The movement can also be detected in
the Z direction, though much weaker.

[0101]Referring to FIG. 9, a measurement obtained during typing on a
keyboard results in a an extremely weak signal, as compared to an
epileptic signal. The signal shown in FIG. 9 is highly random, and has
little repeatability.

[0102]The analog signal outputs of the accelerometers have a DC-biased
component, which is preferably corrected for. This DC bias stems from two
sources--the accelerometer DC output plus the Gravitation `g`. The DC
component is preferably translated to an offset signal following
autocorrelation. In a first step of the signal processing, the DC bias is
calculated and deducted from the digital signal, leaving only "pure"
movement signals.

[0103]To remove only the DC inherent in the sensor, a constant DC removal
is enough. However, preferably. the gravitational DC influence is removed
using a moving window averaging process. This time window should be short
enough to follow the spatial changes of the sensor `g`, while long enough
so as not to influence the spiky epileptic behavior. Thus in the
preferred embodiment, a moving average (MA) is chosen as the DC removal
method, changing the signals according to the equation:

[0105]Autocorrelation is a mathematical tool for exploring the
repeatability of a signal. It is the summation of the signal multiplied
by itself with a short time shift. The result, as a function of this time
lag, emphasizes the periods where the signals resembles itself, while
diminishes the periods with opposite phases. The equation of the
autocorrelation of a real signal is

[0106]A classic illustration of autocorrelation is that seen with a highly
repetitive signal, such as measured during beating on a table, as shown
in FIG. 10. The beating frequency is about 1.5 Hz, i.e. a time period of
0.67 seconds, which is also the time period between the autocorrelation
peaks. The peaks are located at the signal intrinsic time period, while
their values are measurements of the resemblance of the signal to itself
at different times. The value of the middle peak, always at no-delay, is
an indicator for the signal power. For true non-random signals, like
those described herein in relation to the Figures of the invention, the
autocorrelation is always symmetric around this central peak.

[0107]Autocorrelation may be performed upon a signal originating in a
single axis (direction of movement), or correlation may be performed
between the signals originating from several axes.

[0108]In the preferred embodiment, the relative energy level is one
parameter utilized in the invention to identify the type of movement.
Other preferred parameters include the Frequency, Frequency
Stability/variations and Amplitude (energy) Variation between the 3 axes.
These calculations are typically performed as long as the stream of
signal continues, and based on periodical statistical evaluation of the
parameters, a decision is made as to the type of the movement and if and
when to transmit an Epilepsy Attack Alert signal.

[0109]FIGS. 10 through 19 illustrate some parameters useful for epilepsy
seizure detection. The parameters and graphs are shown after DC removal
has been performed from the data recorded by the accelerometers. The
averaging time frame selected in the graphs is approximately 0.4 seconds.

[0110]Referring to FIG. 10, a "Beating on the table" movement was
performed by a healthy individual, and the autocorrelation was plotted.

[0111]The top graph shows the digitized recorded signal of one of the
channels. The central graph of FIG. 10 shows the signal's
autocorrelation, and the middle portion of the signal appears in the
third (lower-most) graph of FIG. 10. Three peaks are defined on the
autocorrelation, which can be seen in the third graph:

[0112]The central peak is also known as the zero peak, the main peak or
the zero-lag peak. As indicated by these names, the peak is located in
the middle of the autocorrelation. It is always the highest peak,
carrying the signal's power information. The autocorrelation is
symmetrical around this peak. The autocorrelation may be normalized to
its value.

[0113]The first peak--the two symmetrical peaks surrounding the central
peak. Its time is the first time period of the signal's repetition. Its
value, relative to the value of the main peak, yields the similarity
between signal's time periods.

[0114]The second peak, which is the pair of identical peaks after the
first peak, give further information about the signal's similarity to
itself after a longer time lag.

[0115]The following parameters are plotted in the attached graphs:

[0116]v0--the value of the central peak

[0117]v1--the value of the first peak, relative to the main peak

[0118]t1--the time lag of the first peak

[0119]v2--the value of the second peak, relative to the main peak

[0120]t2--the time lag of the second peak

[0121]v2/v1--the value of the second peak, relative to the first peak

[0122]t2/t1--the time lag of the second peak, relative to the lag of the
first peak.

[0123]In these graphs, the parameters were calculated on a 15 second
autocorrelation moving window, though in the in-depth analysis other time
frames, like 2 seconds, were also used. For clarity, not all the axes of
the signals are plotted.

[0124]FIG. 11 is an example of 3 axial epileptic signals, when zooming in
on a portion of the epileptic seizure. One can observe the quick, strong
fluctuations, mostly in the XY axes. Each spike resembles the previous
one, but the peaks and the time between them change with time. This
behavior is illustrated in the previously defined parameters, which are
plotted at the following graphs, starting prior to the seizure onset.

[0125]Referring to FIG. 12, the motion signal parameters are illustrated
of acceleration occurring in a single direction, as measured during a
seizure. The values plotted (t1, v1, v0, etc.), were previously defined
in relation to FIG. 10 hereinabove. The signal amplitude of the central
peak (v0) is high only during the seizure (the second graph) (for
example, above 1000). When the autocorrelation is weak, the first and
second peaks are hardly detected, and their parameters are set to zero.
t1 and t2 during epileptic seizures have characteristic values, below 2
and 4 seconds, respectively. As one can see, the values of these
peaks--v1 and v2--are high at the seizure, such as above 0.3. The second
peak is weaker than the first one, but strong enough, thus their ratio
can be in the range [0.5, 1].

[0126]Referring to FIG. 13, similar parameters can be seen in another
recorded signal, of another, much quicker and stronger epileptic seizure.
In this seizure, the movement was mostly in the Y direction, as can be
seen from its much higher acceleration values. Several behavioral changes
occurred during this seizure, as one can see in the zooming in axial
graphs. The acceleration was very quick at first, while later the
acceleration became weaker and the repeatability--slower, with a nearly
quiet period at the center of the enlarged portion.

[0127]Referring to FIG. 14, the parameters are illustrated of acceleration
occurring in a single direction, as measured during a seizure. A similar
gap can also be seen in the parameters graphs, which begin prior to the
seizure onset. The parameters also demonstrate that this is a much
quicker and stronger epileptic motion.

[0128]Referring to FIG. 15, motion signal parameters are depicted for two
cases. The graphs at left (under the Ml heading) refer to one patient's
seizures, while the graphs at right (under the AM heading) refer to
another patient's seizures. Another parameter that can be used for
seizure detection is the dominant axis changes during the seizure. Unlike
a common movement, which generally has a defined direction over certain
time period, the epileptic movement generally changes its spatial
orientation frequently. Adding another highly active channel to the
graphs above of the two epileptic seizures demonstrates this, when in the
left size--MI recording, the X and Y axis are alternately the dominant
movement axis (as seen in the axial graphs above). Similarly, in the AM
recording, Y is the dominant axis, and in a short portion of the seizure,
the X acceleration is also very strong. The same can happen for all the
axes, including the Z axis.

[0129]Referring to FIG. 16, the acceleration signals are depicted for a
normal, non-epileptic motion, occurring during sleep, and termed a "night
stretch" (stretching of the limbs occurring during normal sleep). The
movement is similar to that occurring during epileptic event: [0130]the
physiological aspect--muscle tonus [0131]the mathematical point of
view--the right frequency and amplitude ranges.

[0132]Referring to FIG. 17, the signal parameters of the night-stretch are
depicted, showing a tonic-like movement at a relatively low amplitude.
The signal can be defined as non-epileptic due to its shortened duration.

[0133]Referring to FIG. 18, another daily movement is typing on the
keyboard. The signal is shown in the graph at top left, while the signal
motion parameters are shown in the remaining graphs. The signal
associated with this normal movement is very weak, with low
repeatability, and thus can be easily distinguished from an epileptic
signal occurring during an epileptic event. This is the situation with
most common movements.

[0134]Referring to FIG. 19, these daily movements also exhibit a much
clearer axial direction, as can be seen when adding another axial signal.

[0135]In the night-stretch, the movement is in the XY axes, without short
jolts in other directions, like the one seen in the epileptic recordings.

[0136]The typing signal is much too random and weak, thus no clear v1 can
be detected, not to mention defining a directional behavior.

[0137]Embodiments of the present invention may include apparatuses for
performing the operations herein. This apparatus may be specially
constructed for the desired purposes, or it may comprise a general
purpose computer selectively activated or reconfigured by a computer
program stored in the computer. Such a computer program may be stored in
a computer readable storage medium, such as, but is not limited to, any
type of disk including floppy disks, optical disks, CD-ROMs,
magnetic-optical disks, read-only memories (ROMs), random access memories
(RAMs) electrically programmable read-only memories (EPROMs),
electrically erasable and programmable read only memories (EEPROMs),
magnetic or optical cards, or any other type of media suitable for
storing electronic instructions, and capable of being coupled to a
computer system bus.

[0138]The processes and displays presented herein are not inherently
related to any particular computer or other apparatus. Various general
purpose systems may be used with programs in accordance with the
teachings herein, or it may prove convenient to construct a more
specialized apparatus to perform the desired method. The desired
structure for a variety of these systems will appear from the description
below. In addition, embodiments of the present invention are not
described with reference to any particular programming language. It will
be appreciated that a variety of programming languages may be used to
implement the teachings of the inventions as described herein.

[0139]The system of the invention may be used to detect additional
pathologies related to motion events; there is no intention to limit the
scope of the invention for use with epilepsy alone, rather additional
pathological motion-related events may be detected by the system. A set
of motion signal parameters of normal non-pathological motion may be
stored in the non-volatile memory, as well as motion signal parameters
associated with pathological motion. These may be used and compared to
signal parameters for an individual as measured by the motion sensors of
the invention. Thus, additional motion-related pathologies may be
detected.

[0140]While certain features of the invention have been illustrated and
described herein, many modifications, substitutions, changes, and
equivalents will now occur to those skilled in the art. It is, therefore,
to be understood that the appended claims are intended to cover all such
modifications and changes as fall within the true spirit of the
invention.